AbstractIn humans, resting-state functional connectivity MRI (fcMRI) allows precisein vivo delineation of the neocerebellum’s participation in well-segregated, non-motor intrinsic connectivity networks (ICNs). These data reveal that the neo-cerebellum participates in several ICNs, including the default mode network(lobule IX), the salience network (lobule VI), and the right and left executivenetworks (crus I and II). Additionally, fcMRI permits an anatomical parcellationof the neocerebellum based on its specific functional links with the associativecortex. Lobules V, VII, IX, and especially crus I and II constitute a supramodalcognitive zone specifically interconnected with prefrontal, parietal, and cingulateneocortices. Structural connectivity using DTI-based tractography complements
C. Habas (*)Service de NeuroImagerie, CHNO des XV–XX, Université Pierre et Marie Curie Paris 6, Paris,Francee-mail: [email protected]
W. R. Shirer · M. D. GreiciusDepartment of Neurology and Neurological Sciences, Functional Imaging in NeuropsychiatricDisorders (FIND) Lab, Stanford University School of Medicine, Stanford, CA, USAe-mail: [email protected]; [email protected]
fcMRI data and confirms anatomical connections between the dentate nucleus,thalamus, and associative cortices. Taken together, these results support thetheory that specific neocerebellar subregions are key nodes in parallel, multi-synaptic, closed-loop circuits involved in executive, mnemonic, and affectivefunctions.
In primates, the cerebrocerebellar system is organized into discrete, parallel, multi-synaptic, closed-loop circuits (Strick et al. 2009). A common gross anatomicalconnectivity pattern is shared by all these circuits. The cerebral cortex selectivelyprojects via the pontine nuclei (PN) to the contralateral deep cerebellar nuclei,mainly the dentate nuclei (DN), and to the associated cerebellar cortex. The DN,in turn, send projections to the cerebral cortex via the contralateral thalamus (Vincentet al. 2003; Nieuwenhuys et al. 2007). These cerebro-ponto-cerebello-thalamo-cerebral networks specifically and differentially influence motor, premotor, andassociation cortices (Middleton and Strick 1997; Schmahmann and Pandya 1997).For instance, tracing methods demonstrate that dorsal, lateral, and ventral parts of theDN are specifically connected with frontal motor, premotor, and prefrontal cognitiveregions, respectively (Middleton and Strick 2001; Dum and Strick 2003; Akkal et al.2007) (Fig. 1). Moreover, motor areas preferentially connect with the anteriorcerebellar lobe (lobules I–V) and lobule VIII (second cerebellar homunculus),whereas executive and limbic areas are mostly connected with the posterior lobe(lobules VI and VII), which is densely interconnected with the DN.
Moreover, the vermis of lobules VB–VIIIB receives afferents arising from severalmotor areas, including M1, supplementary motor area, and motor cingulate cortex(Coffman et al. 2011), and the neocerebellum and striatum were interconnected(Hoshi et al. 2005; Bostan et al. 2010). More precisely, DN projects through thethalamus to the striatum and to the external portion of the globus pallidus, while thesubthalamic nucleus projects via the pontine nuclei to the cerebellar cortex of crus IIand of lobule VIIB.
From apes to humans, the telencephalization process is accompanied by anincreasing number of nonmotor cerebral afferents reaching the neocerebellum (lob-ules VI and VII) (MacLeod et al. 2003; Whiting and Barton 2003). It can thereforebe inferred that in humans, the neocerebellum contributes to parallel associativecerebrocerebellar subsystems involved in various aspects of cognition and emotion(Schmahmann 2004). The hypothesis concerning topographic arrangement of motorand nonmotor function in the cerebellum was proposed by Schmahmann (1991,2004) who regarded the cerebellum as a general modulator of cerebral activity, with
2 C. Habas et al.
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Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 3
the anterior and posterior cerebellum supporting and refining motor and cognitiveperformance, respectively. In support of this view, neuroimaging studies have shownspecific cerebellar activation during emotion, language, working memory, andexecutive function (reviewed in: Stoodley and Schmahmann 2008), while clinicaldata have substantiated several but sometimes variable, cognitive, and affectiveimpairments in patients suffering from focal cerebellar lesions (Schmahmann andShermann 1998; Levisohn et al. 2000; Tedesco et al. 2011).
Despite evidence from functional imaging and lesion studies, cerebellar involve-ment in cognition remains a matter of debate. For instance, some studies failed tofind significant attentional or semantic impairment in cerebellar patients (Helmuthet al. 1997; Thier et al. 1999; Haarmeier and Thier 2007). Furthermore, in chronicpatients, standard neuropsychological tests often turn up only minor (if any) cogni-tive impairments even though motor deficits are readily detected. These inconsis-tencies in cognitive impairments could be attributed to several factors, such ascompensation for cerebellar disorders by unaffected cerebellar and cerebral regionsor stronger involvement of the cerebellum during “early cognitive developmentrather than during cognitive performance in adulthood” (Timmann and Daum 2010).
However, until recently, the nonmotor, probably genetically prewiredcerebrocerebellar circuits subserving these cognitive and emotional functionscould not be directly and completely identified in humans because of two limitations.First, the gold standard histological tracing methods used in animals cannot beapplied to humans. Second, standard fMRI studies using the general linear modelonly discriminate a limited number of highly task-specific brain areas whose bloodoxygenation level-dependent (BOLD) signal time series closely mimics the temporalmodel of the experimental task (van Dijk et al. 2010). Thus, in those cases where, forexample, a cerebellar region’s BOLD signal displays a complex or subthresholdrelation to the task waveform, fMRI may underestimate the number of nodescomprising a task-specific network.
Recently, two complementary MRI methods have been developed which overcomethese limitations and allow for functional and structural identification of large-scalebrain networks: resting-state functional connectivity MRI (fcMRI) and diffusiontensor imaging (DTI) tractography. fcMRI relies on temporal correlations betweenspontaneous low-frequency (0.01–0.1 Hz) fluctuations of the BOLD signal betweenspatially distinct but functionally related cortical and subcortical regions (Beckmannet al. 2005; Fox and Raichle 2007). Regions with synchronous spontaneous activityconstitute intrinsic connectivity networks (ICNs) and may be linked by mono- orpolysynaptic pathways (Greicius et al. 2008; Vincent et al. 2003). The degree towhich these spontaneous fMRI signal fluctuations reflect ongoing conscious pro-cessing rather than, for example, nonconscious rhythmic waves of cortical excitabil-ity is a matter of continuing debate. Generally, two methods are used to extract theseICNs from raw fcMRI data. First, independent component analysis (ICA) is anexploratory, model-free, data-driven statistical method, which transforms thewhole resting-state dataset into maximally independent spatial components(Beckmann and Smith 2004). Each component consists of a spatial map and itsassociated time series. In a typical ICA of an 8-min resting-state fMRI dataset, there
4 C. Habas et al.
may be 40 components computed, of which 10–15 may represent genuine ICNs,based on their resemblance to well-characterized task activation networks. Theremaining 25–30 represent various noise sources that can also result in correlatedBOLD signal fluctuations. The second method uses a region of interest (ROI) as aseed for a whole-brain correlation analysis. That is, the mean (or major Eigen) timeseries of an ROI is extracted and used as a regressor to search the brain for othervoxels whose time series is significantly correlated with the ROI. This results in amap of functional connectivity to the ROI.
The ICNs derived from resting-state fMRI studies have been shown to overlap toa large degree with maps of structural connectivity derived from DTI tractographyanalyses (Skudlarski et al. 2008; Greicius et al. 2009; van den Heuvel et al. 2009).Therefore, fcMRI identifies functionally related areas belonging to a commonspecialized structural network, whose anatomical architecture can only beestablished by tractography (within the limits of its spatial resolution and of itsability to detect fiber crossings). However, it is noteworthy that despite a strongcorrelation between functional and structural connectivity, especially concerning theDMN, topography of ICNs can be influenced by previous or ongoing cognitiveprocessing (Hasson et al. 2009; Shirer et al. 2011) and can exhibit variabilitybetween sessions and individuals (Honey et al. 2009). Thus, ICNs may representpolysynaptic circuits and may continually reconfigure around the underlying ana-tomical skeleton (Honey et al. 2009).
Resting-state connectivity of the cerebellar system has successfully been studiedwith both ROI-based and ICA-based methods. The ROI-based method was appliedto the cerebellar cortex in order to delineate cerebellar subregions preferentiallyassociated with the dentate nucleus as well as with motor, sensory, and associativecerebral cortical areas. ICA was used to demonstrate which cerebellar regions wereassociated with which ICNs.
Functional Connectivity
ROI-Based fcMRI of the Dentate Nucleus
Functional connectivity (Allen et al. 2005) was found between the left dentatenucleus, and (1) the right DN, (2) the cerebellar cortex bilaterally (anterior andposterior vermis and hemispheres), (3) the thalamus bilaterally (ventral anterior,ventral lateral) but right dorsomedial nucleus, (4) the striatum (caudate nucleibilaterally and right putamen), (5) the right limbic cortex (insula, hippocampus,and parahippocampus), (6) the right posterior cingulate cortex, (7) the right medialoccipital lobe, (8) the inferior parietal lobe (BA 39/40), (9) the right (para-)cingulatecortex (BA 24/32), (10) the dorsolateral prefrontal cortex bilaterally (BA 8/9/46),and (11) the frontal pole (BA 10). Functional connectivity was found between theright dentate nucleus, and (1) the left DN, (2) the bilateral cerebellar cortex (anteriorand posterior vermis and hemispheres) including the fastigial/globose nuclei, (3) thethalamus bilaterally (ventral anterior, ventral lateral, dorsomedial), (4) the left
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 5
hypothalamus, (5) the striatum (caudate nucleus, putamen, and pallidum bilaterally),(6) the right insula (BA 13), (7) the anterior cingulate cortex bilaterally (BA 24) witha right predominance, (8) the left occipital cortex (BA 19), and (9) the dorsolateralprefrontal cortex (BA 9/46 with an extension to BA 10). Although fcMRI dataprovide no information about the directionality of the connectivity, some of theseregions may correspond to mono- or disynaptic targets of the DN. In monkeys, theDN projects to the motor (BA 4), premotor (BA 6 medial, i.e., (pre-)SMA, andlateral), prefrontal (BA 9/46), and posterior parietal associative brain areas via thethalamus (ventral lateral and dorsomedial) (Strick et al. 2009). The DN also targetsthe striatum (Hoshi et al. 2005) and the hypothalamus (Haines et al. 1997). There-fore, fcMRI of the DN may have functionally traced associative dentato-thalamo-cortical, dentato-striatal, and dentato-hypothalamic circuits. The remaining areasfunctionally connected with the DN in this study could be polysynaptic (two ormore) relays or phylogenetically new relays of these circuits. It cannot be ruled outthat these regions also send afferents to DN and the overlying cerebellar cortex.However, these cerebral afferents reach the cerebellum via a relay in the PN (andbulbar olivary nucleus and reticular nuclei), which were not detected in this study.This could argue in favor of a preferential detection of functional connectivity of thecerebellar output channel. Alternatively, this lack of detection of the PN may beexplained by a threshold problem, low sensitivity technique (1.5 T), or low spatialresolution.
ROI-Based fcMRI of the Cerebellar Cortex
The first cerebellar fcMRI study was performed by He et al. (2004) examiningconnectivity between the anterior inferior cerebellum and the rest of the brain.However, no exact location of the cerebellar seed region was provided, and, whenreferring to the figure, this region seems to be located inside the cerebellar whitematter. Therefore, this study was not included in the current chapter. More recently,Sang et al. (2012), using voxel-based and cerebellar ROI-based analyses, providedinteresting results concerning, in particular, functional connectivity of the vermis, onone hand, and between cerebellum and subcortical and midbrain nuclei. Forinstance, they showed strong coherence between vermis (lobules VIIB–IX) and thevisual network, vermis (lobule VIIIB) and default mode network, as well as withcaudate nucleus, crus I and II with caudate nucleus, cerebellum (lobules V, VI, VIIBand VIIIA) and lenticular nucleus, lobules V/VIIB and red nucleus, and lobules I–V/VIII–IX and cerebral amygdala, as well as hippocampus.
ROI-Based fcMRI of the Cerebral Cortex
Krienen and Buckner (2009) and O’Reilly et al. (2009) defined an anatomicalparcellation of the cerebellar cortex based on their specific coherence with distinctcortical ROIs and using the probabilistic cerebellar atlas of Diedrischsen et al.
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(2009). Krienen and Buckner found correlations between the dorsolateral prefrontalcortex and lobule VII (crus I and II and VIIB, especially crus II), the medialprefrontal cortex and lobule VII (crus I), and the anterior prefrontal cortex andlobules VI and VII (crus I/II/VIIB/VIIIA) (Fig. 2). O’Reilly and colleagues corrob-orated these observations by showing that motor, premotor, and somatosensorycortices were correlated with the cerebellar anterior lobe (lobules V/VI/VIII),
Fig. 2 Functional anatomicparcellation of the humancerebellum based on resting-state functional connectivityusing ROIs located in motorcortex (MOT), dorsolateralprefrontal cortex (DLPFC),medial prefrontal cortex(MPFC), and anteriorprefrontal cortex (APFC).Caudal view (top). Rostralview (middle). Dorsal view(bottom). (From Krienen andBuckner 2009; with authors’permission)
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 7
whereas the prefrontal cortex was functionally connected with the posterior lobe(paravermal and lateral hemisphere of lobule VIIA including crus I and II). Strongcorrelations were also detected between visual area MT and lobules V/VI/VIII,superior temporal gyrus including auditory areas with lobules V/VI, and inferiorposterior parietal cortex with lobules VIIA (paravermis) and crus II. Therefore, twomain zones were distinguished in the cerebellum: (1) a primary sensorimotor zone(lobules V/VI/VIII) containing overlapping sensory and motor domains in relation tosomatomotor, visual, and auditory cortices and (2) a supramodal zone (lobule VII)containing overlapping cognitive domains in relation to prefrontal and parietalcortices. The latter associative areas were also mapped in this study and comprisethe posterior frontal medial gyrus (BA 8), the middle medial gyrus (BA 9/46), thefrontal pole (BA 10), the inferior parietal lobule (BA 39), the medial superior parietallobule (BA 7b), and the posterior cingulate cortex (BA 25). Moreover, the cerebellarsupramodal zone can be further segregated according to its functional links withdorsolateral, medial, and anterior prefrontal cortex. It is noteworthy that the func-tional connectivity between prefrontal and parietal cortices and neocerebellum is notrestricted to oculomotor regions such as frontal and parietal eye fields or vermian andparavermian parts of lobule VI/VII but rather involves multiple regions in prefrontalcortex, parietal cortex, and neocerebellum. Therefore, it cannot be claimed thatprefronto/parieto-cerebellar interconnections exclusively relate to the oculomotorsystem (Doron et al. 2010). More recently, Buckner et al. (2011) seeded smallregions within the cerebellum in order to precisely determine the functionallycorrelated topography in the cerebral cortex during resting state. In particular, theyestablished that the cerebellum contains at least two topographically organized,inverted representations of the complete cerebrum, with the exception of primaryvisual and auditory cortices. The cerebral cortex, including somatomotor, premotor,and association areas, is functionally linked to (1) a homotopic map extending fromthe somatomotor anterior lobe to crus I and II and (2) a mirror-image secondary mapextending from crus I and II to lobule VIII. If crus I and II, in association with part oflobule VI, and lobules VIIB and IX, are in functional coherence with the associationcortex, the border of crus I and II displayed strong correlations with the default-modenetwork. It was also found that the somatomotor map in the anterior lobe (lobulesIV/V/VI) represents the foot, hand, and tongue in the rostral-to-caudal axis and waslocated close to the vermis. Therefore, medial lobule VI belongs to the somatomotorzone, while the lateral part of this lobule takes part in the supramodal zone.
ICA fcMRI
The abovementioned studies provide a functional anatomic parcellation of thecerebellar cortex and show correlation of the neocerebellum (lobule VII) with theassociative neocortex. However, most of these ROI-based studies did not examinecorrelations between neocerebellum and other parts of the brain and thus could notidentify and segregate all the relays contributing to distinct specialized cerebro-neocerebellar networks. ICA is a method that examines whole-brain connectivity
8 C. Habas et al.
without the priori identification of an ROI. Habas et al. (2009) applied ICA analysisto resting-state fMRI data and used an unbiased template-matching procedure toidentify previously studied ICNs: (1) the default-mode network (DMN) (Greiciuset al. 2003, 2004) involved in stream of consciousness, mental imagery, episodicmemory retrieval, and self-reflection (Raichle et al. 2001); (2) the executive controlnetwork (ECN, divided by ICA into left and right ECNs) involved in workingmemory, attention, response selection, and flexibility (Seeley et al. 2007); (3) thesalience network (Seeley et al. 2007) required for the processing and integration ofinteroceptive, autonomic, and emotional information; and (4) the sensorimotornetwork (Biswal et al. 1995). Distinct cerebellar contributions were found in eachof these ICNs. The neocerebellum was shown to participate in (1) the DMN (lobuleIX), (2) the right and left ECNs (crus I and II with a narrow extension in lobules VIIBand rostral IX), (3) the salience network (lobule VI with narrow extension in lobulesVIIA: crus I and II and VIIB), and (4) the sensorimotor network (lobules V andadjacent VI) (Fig. 3). Three other structures of the cerebrocerebellar system werealso identified: the PN (DMN, ECN, salience network), the DN (sensorimotor,salience networks), and the red nucleus (sensorimotor, DMN, ECN, salience net-work) (Fig. 4). These results are in agreement with O’Reilly et al. (2009) andKrienen and Buckner’s (2009) data, highlighting functional connectivity of lobuleVII (especially crus I and II) with dorsolateral and dorsomedial (BA 9/46) prefrontaland cingulate cortices (ECNs), and frontoinsular cortex (salience network) (Fig. 4).More recently, Brissenden et al. (2016) gave support to the participation of cerebel-lar lobules VIIb/VIIIA to the dorsal attentional network.
The ICA results, however, provide four new pieces of data. First, the cerebellarsupramodal zone can be extended to the caudal part of lobule VI, which is includedin the salience network and can be clearly dissociated from the more anteriorsensorimotor part of the same lobule and to lobule IX participating in the DMN.This is in line with O’Reilly et al. (2009) who also found a correlation between thetonsilla and the prefrontal cortex. Second, the major part of lobule VIIA is devoted tothe ECNs. These results are in accordance with the meta-analysis of cerebellarneuroimaging studies (Stoodley and Schmahmann 2008) showing involvement oflobule VI/VII (crus I) in nonmotor linguistic, spatial, and executive processes. Third,an extracerebellar relay such as the red nucleus also contributes to nonmotor circuits.This is in keeping with Nioche et al. (2009) and supports the view that duringphylogeny, not only the cerebellum but also its associated nuclei have evolved inparallel with the neocortex (Ramnani 2006). Fourth, ICNs encompass cerebellarinput (PN) and output channels (DN) so that an ICN may indeed represent loopsreciprocally linking cerebellum and cerebral cortex (Fig. 5).
Tractography
fcMRI enables identification of the neural relays contributing to a given specializednetwork but cannot distinguish which relays are directly connected by monosynapticlinks. In other words, functional connectivity defined by fcMRI cannot distinguish
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 9
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direct connectivity between two regions from indirect connectivity possibly medi-ated by a third region. Two recent studies have attempted to overcome this drawbackby using DTI-based tractography, a method that allows for reconstruction of whitematter tracts that directly link two neural regions. Habas and Cabanis (2007), Doronet al. (2010), and Kamali et al. (2010) applied tractography to trackcorticopontocerebellar fibers. They found that orbitofrontal, prefrontal, pericentral,parietal, and temporal/occipital cortices project onto specific PN, which, in turn,
Fig. 4 Human cerebellartopography based on resting-state functional connectivityto the frontal cortex (a) (FromKrienen and Buckner 2009)and (b) to five intrinsicallyconnected networks includingsomatomotor, left and rightexecutive control, default-mode, and limbic saliencenetworks (From Habas et al.2009). In (a) (pre-)motorcortex (yellow), parietal cortex(green), dorsal prefrontalcortex (red), and medialprefrontal cortex (orange). In(b) sensorimotor network(purple), default-modenetwork (yellow), right andleft executive control network(red and blue, respectively),and salience network (green).Sps superoposterior sulcus.This figure demonstrates thesubstantial overlap incerebellar functionalconnectivity maps obtainedwith ROI- and ICA-basedparcellation of the cerebellum.Convergent findings includecerebellar regions connectedto the sensorimotor network(yellow in a/purple in b), theexecutive control network(red in a/red and blue in b), thedefault-mode network (yellowin b, especially in lobules IX),and the salience network(orange in a/green in b)
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 11
project via the middle cerebellar peduncle to the cerebellar cortex. More precisely,Doron et al. (2010) only described prefrontal corticopontine fibers arisen fromcaudal and medial superior frontal gyrus and a small region of the medial prefrontalgyrus. However, collaterals of mossy fibers from the PN to deep cerebellar nucleicould not be tracked. Ramnani et al. (2005) compared the organizational origins ofcorticocerebellar fibers in the cerebral peduncle (crus cerebri) between macaques andhumans. This DTI study found a larger contribution to the crus cerebri from theprefrontal cortex in humans than in macaques. Within the cerebellum, the deepcerebellar nuclei, mainly the DN, are targeted by the PN (mossy fibers), bulbarolivary nucleus (climbing fibers), and the cerebellar cortex (Granziera et al. 2009).The DN are directly connected with the red nucleus and indirectly via the ventral partof the thalamus (Habas and Cabanis 2007; Granziera et al. 2009), with the cerebralcortex: sensorimotor (M1/S1), temporal (Habas and Cabanis 2007), prefrontal(BA 9), and parietal (BA 7) (Jissendi et al. 2008) cortices (Fig. 6). DN also project
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12 C. Habas et al.
directly to the thalamus including mainly ventrolateral, posterior, intralaminar,subparafascicular, medioventral and dorsomedian nuclei (Hyam et al. 2012; Pelzeret al. 2017), globus pallidus, mainly ispsilaterally, and to the ipsilateral substantianigra (Milardi et al. 2016) and, indirectly, through the thalamus, to the striatal,pallidal, and subthalamic nuclei (Pelzer et al. 2013). Furthermore, Sokolov et al.(2014) identified a loop linking left crus I and right superior temporal sulcus via thesuperior cerebellar peduncle and the pons. Finally, Arrigo et al. (2014) describedcerebello-limbic interconnections: tractograms were computed between cerebellarvermis, crus I/II and lobules VIII-IX, and hippocampus (subiculum, CA1, andfimbria).
Altogether, these tractography studies confirm connections in humans betweenthe neocerebellum and the associative cerebral cortex and, in particular, closed loopsbetween the neocerebellum, including the DN, and prefrontal and parietal cortices.However, because of their low spatial resolution, their partial coverage of the brain,their low sensitivity to discriminate fiber crossings, and their inability to followtrajectories within low anisotropic regions, these studies may underestimate both thenumber of neocortical areas involved in the cerebrocerebellar system and also thenumber of loops. It is noteworthy that very high-field MRI (7 T) allows to performmicro-tractography visualizing, for example, T-shaped parallel fibers within thecerebellar molecular layer and that algorithm such as spherical deconvolution-based processing can detect fiber crossings (Dell’Acqua et al. 2013).
Therefore, fcMRI and tractography (i.e., functional and structural connectivity)are complementary in deciphering functional networks.
Fig. 6 DTI-baseddeterministic tractography ofthe dentate nuclei showing, ona sagittal slice, dentato-thalamo-cortical projectionsending within the prefrontal(yellow fibers) and superiorparietal (green fibers) cortices.(From Jissendi et al. 2008;with authors’ permission)
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 13
Conclusions and Future Directions
fcMRI enables functional anatomic parcellation of the cerebellum into well-segregated subregions which participate in specific, functionally distinct, large-scale cerebrocerebellar networks. DTI tractography provides a complementaryapproach which can be used to help distinguish direct from indirect connections inthe functional maps. Using these approaches, the cerebellar cortex can be subdividedinto a polymodal sensorimotor zone (lobules IV/V/VI and VIII) and a supramodalcognitive zone (lobules VI/VII and, especially, crus I and II, and lobule IX). Sub-regions of the cognitive neocerebellum take part in the DMN (lobule IX), thesalience network (lobule VI), and the right/left ECNs (crus I and II). Strong func-tional links exist between crus I and II and prefrontal, parietal, and cingulate cortices,supporting the role of the most phylogenetically recent part of the cerebellum inexecutive and affective functions. These intrinsically connected networks variablyinclude the DN and PN. Lack of detection of the PN/DN in certain circuits could beascribed to the stringent statistical postprocessing of the fcMRI data. It also cannotbe ruled out that connectivity between the cerebellum and other parts of the brainmay be mediated by the bulbar olivary, lateral vestibular, and reticular nuclei, as wellas via the striatum (Hoshi et al. 2005; Bostan et al. 2010). It is worth noting that nofunctional connectivity was detected for the posterior vermis, especially lobule VIIinvolved in limbic emotional processing, regardless of the fcMRI method performed.Thus, while functional and structural connectivity approaches have helped us beginto delineate the functional anatomy of the cerebellum, there is still much work to do.
References
Akkal D, Dum RP, Strick PL (2007) Supplementary motor area and presupplementary motor area:targets of basal ganglia and cerebellar output. J Neurosci 27:10659–10673
Allen G, McColl R, Holly B, Ringe WK, Fleckenstein J, Cullum CM (2005) Magnetic resonanceimaging of cerebellar-prefrontal and cerebellar-parietal functional connectivity. Neuroimage28:39–48
Arrigo A, Mormina E, Anastasi GP, Calamuneri A, Quartarone A, De Salvo S, Bruschetta D, RizzoG, Trimarchi F, Milardi D (2014) Constrained spherical deconvolution analysis of the limbicnetwork in human, with emphasis on a direct cerebello-limbic pathway. Front Hum Neurosci8:987. https://doi.org/10.3389/fnhum.2014.00987. ECollection 2014
Beckmann CF, Smith SM (2004) Tensorial extension of independent component analysis for multi-subject FMRI analysis. IEEE Trans Med Imaging 23:137–152
Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connec-tivity using independent component analysis. Philos Trans R Soc Lond Ser B Biol Sci360:1001–1013
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex ofresting human brain using echo-planar MRI. Magn Reson Med 34:537–541
Bostan AC, Dum RP, Strick PL (2010) The basal ganglia communicate with the cerebellum. ProcNatl Acad Sci U S A 107:8452–8456
Brissenden JA, Levin EJ, Osher DE, Halko MA, Somers DC (2016) Functional evidence for acerebellar node of the dorsal attention network. J Neurosci 36(22):6083–6093
Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT (2011) The organization of the humancerebellum estimated by intrinsic functional connectivity. J Neurophysiol 106(3):1125–1165
Coffman KA, Dum RP, Strick PL (2011) Cerebellar vermis is a target of projections from the motorareas in the cerebral cortex. Proc Natl Acad Sci U S A 108(38):16068–16073
Dell’Acqua F, Bodi I, Slater D, Catani M, Modo M (2013) MR diffusion histology and micro-tractography reveal mesoscale features of the human cerebellum. Cerbellum 12(6): 923–931
Diedrischsen J, Balsters JH, Flavell J, Cussans E, Rammani N (2009) A probabilistic MR atlas ofthe human cerebellum. Neuroimage 46:39–46
Doron KW, Funk CM, Glickstein M (2010) Fronto-cerebellar circuits and eye movement control: adiffusion imaging tractography study of human cortico-pontine projections. Brain Res1307:63–71
Dum RP, Strick PL (2003) An unfolded map of the cerebellar dentate nucleus and its projection tothe cerebral cortex. J Neurophysiol 89:634–639
Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functionalmagnetic resonance imaging. Nat Rev Neurosci 8:700–711
Granziera C, Schmahmann JD, Hadjikhani N, Meyer H, Meuli R, Wedeen V, Krueger G (2009)Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in thehuman cerebellum system in vivo. PLoS One 4:1–6
Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: anetwork analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 100:253–258
Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distin-guishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl AcadSci U S A 101:4637–4642
Haarmeier T, Thier P (2007) The attentive cerebellum – myth or reality? Cerebellum 6:177–183Habas C, Cabanis EA (2007) Cortical projections to the human red nucleus: complementary results
with probabilistic tractography at 3T. Neuroradiology 49:777–784Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, Greicius MD (2009) Distinct
cerebellar contributions to intrinsic connectivity networks. J Neurosci 29(26):8586–8594Haines DE, Dietrichs E, Mihailoff GA, McDonald EF (1997) Cerebellar-hypothalamic axis: basis
circuits and clinical observations. Int Rev Neurobiol 41:84–107Hasson U, Nusbaum HC, Small SL (2009) Task-dependent organization of brain regions active
during rest. Proc Natl Acad Sci U S A 106(26):10841–10846He Y, Zang Y, Jiang T, Liang M, Gong G (2004) Detecting functional connectivity of the
cerebellum using low frequency fluctuations (LFFs). LNCS 3217:907–915Helmuth LL, Ivry RB, Shimizu N (1997) Preserved performance by cerebellar patients on tests of
word generation, discrimination learning and attention. Learn Mem 3:456–474Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P (2009) Predicting
human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U SA 106:2035–2040
Hoshi E, Tremblay L, Féger J, Carras PL, Strick PL (2005) The cerebellum communicates with thebasal ganglia. Nat Neurosci 8:1491–1493
Hyam JA, Owen SLF, Kringelbach ML, Jenkinson H, Stein JF, Green AL, Aziz TZ (2012)Contrasting connectivity of the ventralis intermedius and ventralis oralis posterior nuclei ofthe motor thalamus demonstrated by probabilistic tractography. Neurosurgery 70(1):162–169
Jissendi P, Baudry S, Balériaux D (2008) Diffusion tensor imaging (DTI) and tractography of thecerebellar projections to prefrontal and posterior parietal cortices: a study at 3T. J Neuroradiol35:42–50
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 15
Kamali A, Kramer LA, Frye RE, Butler IJ, Hasan KM (2010) Diffusion tensor tractography of thehuman brain cortico-ponto-cerebellar pathways: a quantitative preliminary study. J Magn ResonImaging 32(4):809–817
Levisohn L, Cronin-Golomb A, Schmahmann JD (2000) Neuropsychological consequences ofcerebellar tumour resection in children. Cerebellar cognitive affective syndrome in paediatricpopulation. Brain 123:1041–1050
MacLeod CE, Zilles K, Schleicher A, Rilling JK, Gibson KR (2003) Expansion of the neo-cerebellum in Hominoidea. J Hum Evol 44:401–429
Middleton FA, Strick PL (1997) Dentate output channels: motor and cognitive components. ProgBrain Res 114:555–568
Middleton FA, Strick PL (2001) Cerebellar projections to the prefrontal cortex of the primate.J Neurosci 15:700–712
Milardi D, Arrigo A, Anastasi G, Cacciola A, Marino S, Mormina E, Calamuneri A, Bruschetta D,Cutroneo G, Trimarchi F, Quartarone A (2016) Extensive direct subcortical cerebellum-basalganglia connections in human brain as revealed by constrained spherical deconvolutiontractography. Front Neurosci 10:29
Nieuwenhuys R, Voogd J, van Huijzen C (2007) The human central nervous system: a synopsis andatlas, 4th edn. Springer, New York
Nioche C, Cabanis EA, Habas C (2009) Functional connectivity of the human red nucleus in thebrain resting state at 3T. Am J Neuroradiol 30:396–403
O’Reilly JX, Beckmann CF, Tomassini V, Ramnani N, Johansen-Berg H (2009) Distinct andoverlapping functional zones in the cerebellum defined by resting state functional connectivity.Cereb Cortex 20:953–965
Pelzer EA, Hintzen A, Goldau M, von Cramon DY, Timmermann L, Tittgemeyer M (2013)Cerebellar networks with basal ganglia: feasibility for tracking cerebello-pallidal andsubthalamo-cerebellar projections in the human brain. Eur J Neurosci 38:3106–3114
Pelzer EA, Melzer C, Timmermann L, von Cramon DY, Tittgemeyer M (2017) Basal ganglia andcerebellar interconnectivity within the thalamus. Brain Struct Funct 22(1): 381–392
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A defaultmode of brain function. Proc Natl Acad Sci U S A 98(2):676–682
Ramnani N (2006) The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci7:511–522
Ramnani N, Behrens TEJ, Johansen-Berg H, Richter MC, Pinsk MA, Andersson JLR, Rudebeck P,Ciccarelli O et al (2005) The evolution of prefrontal inputs to the cortico-pontine system:diffusion imaging evidence from macaque monkeys and humans. Cereb Cortex 16:811–818
Sang L, Qin W, Liu Y, Han W, Zhang Y, Jiang Y, Yu C (2012) Resting-state functional connectivityof the vermal and hemispheric subregions of the cerebellum with both the cerebral corticalnetworks and subcortical structures. NeuroImage 61(4):1213–1225
Schmahmann JD (1991) An emerging concept: the cerebellar contribution to higher function. ArchNeurol 48:1178–1187
Schmahmann JD (2004) Disorders of the cerebellum: ataxia, dysmetria of thought, and thecerebellar cognitive affective syndrome. J Neuropsychiatr Clin Neurosci 16:367–378
Schmahmann JD, Pandya DN (1997) The cerebrocerebellar system. Int Rev Neurobiol 41:31–60Schmahmann JD, Shermann JC (1998) The cerebellar cognitive and affective syndrome. Brain
(2007) Dissociable intrinsic connectivity networks for salience processing and executive con-trol. J Neurosci 27:2349–2356
Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD (2011) Decoding subject-drivencognitive states with whole-brain functional connectivity patterns. Cereb Cortex 22:158–165
16 C. Habas et al.
Skudlarski P, Jagannathan K, Calhoun VD, Hampson M, Skudlarska BA, Pearlson G (2008)Measuring brain connectivity: diffusion tensor imaging validates resting state temporal corre-lations. Neuroimage 43:554–561
Sokolov AA, Erb M, Grodd W, Pavlova MA (2014) Structural loop between the cerebellum and thesuperior temporal sulcus: evidence from diffusion tensor imaging. Cereb Cortex 24:626–632
Stoodley CJ, Schmahmann JD (2008) Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 44:489–501
Strick PL, Dum RP, Fiez JA (2009) Cerebellum and nonmotor function. Annu Rev Neurosci32:413–434
Tedesco AM, Chiricozzi FR, Clausi S, Lupo M, Molinari M, Leggio MG (2011) The cerebellarcognitive profile. Brain 134:3669–3683
Thier P, Haarmeier T, Treue S, Barash S (1999) Absence of a common functional denominator ofvisual disturbances in cerebellar disease. Brain 122(Pt 11):2133–2146
Timmann D, Daum I (2010) How consistent are cognitive impairments in patients with cerebellardisorders. Behav Neurol 21:1–21
van den Heuvel MP, Mandl RCW, Kahn RS, Pol HEH (2009) Functionally linked resting-statenetworks reflect the underlying structural connectivity architecture of the human brain. HumBrain Mapp 30:3127–3141
van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL (2010) Intrinsicfunctional connectivity as a tool for human connectomics: theory, properties, and optimization.J Neurophysiol 103:297–321
Vincent JL, Pratel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Voogt J (2003)The human cerebellum. J Chem Neuroanat 26:243–252
Whiting BA, Barton RA (2003) The evolution of the cortico-cerebellar complex in primatesanatomical connections predict patterns of correlated evolution. J Hum Evol 44:3–10
Delineation of Cerebrocerebellar Networks with MRI Measures of Functional. . . 17
